Jan 16
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About This Video Unlock the hidden layer of modern AI systems in this power-packed session by Sivasubramanian Bagavathiappan, Co-Founder of GuhaTek, as he breaks down the real meaning of LLM Observability — and why it has become a non-negotiable capability for teams deploying AI in production. This deep dive covers: • How to trace prompts, reasoning paths, and model interactions • Why LLMs make the decisions they do • Instrumentation techniques for debugging and monitoring LLM systems • How observability increases reliability, safety, and transparency • What every AI engineer should know before scaling LLM applications If you’re building with LLMs, you cannot scale what you cannot see. This session is your blueprint for designing observable, trustworthy, and production-ready AI. 🔔 Subscribe for more advanced breakdowns on AI engineering, MLOps, and real-world LLM systems. Stay Connected 🔗 Instagram: https://www.instagram.com/guhatek?igsh=MWVrb244djZtMmllYg== 🔗 LinkedIn: https://www.linkedin.com/company/guhatek 🔗 Facebook: https://www.facebook.com/share/17HztfDMBj/?mibextid=wwXIfr 🔗 Website: https://www.guhatek.com/ 🔗 X : https://x.com/GuhaTek_Social CHAPTERS: 00:00 – Introduction 01:26 – LLM Architecture and Internals 13:44 – Visibility is Control 20:29 – Open Source Frameworks and Tools 31:53 – Observability of AI Agents 34:00 – llm-observability - GitHub Page 34:35 – Closing Insights Subscribe for more SRE, DevOps & Cloud content! #GuhaTek #SRE #DevOps #Cloud #Kubernetes #Automation #LLMObservability #Siva #AIEngineering #MLOps #ExplainableAI #AIDebugging #ProductionAI #AITransparency #FutureOfAI LLM Observability, AI observability, LLM debugging, production AI monitoring, tracing LLM prompts, AI reliability, LLM instrumentation, Siva GuhaTek, explainable AI systems, AI pipelines, LLM engineering, MLOps workflows